We propose a novel approach to improve the distinctiveness of local image features without significantly affecting their robustness with respect to image deformations. Local image features have proven to be successful in computer vision tasks involving partial occlusion, background noise, and various types of image deformations. However, the relatively high number of outliers that have to be rejected from the correspondences set, formed during the search for similar features, still plagues this approach. The task of rejecting outliers is usually based on estimating the global spatial transform suffered by the features in the correspondences set. This presents two problems: (i) it cannot properly deal with non-rigid objects, and (ii) it is sensitive to a high number of outliers. Here, we address these problems by combining typical local features with shape context. A performance evaluation shows that this new semi-local feature generally provides higher distinctiveness and robustness to image deformations, thus potentially increasing the inlier/outlier ratio in the correspondences set. Also, we show that in wide baseline stereo matching, and non-rigid motion applications, the use of the novel semi-local feature not only provides robustness to non-rigid deformations, but also produces a higher inlier/outlier ratio than the standard Hough clustering of the global spatial transform of parameters.